Sample release

This notebook serves as a basic introduction to loading and viewing data released in associaton with the GWTC-2 Parameter Estimation Data Release.

The released data file can be read in using the h5py, PESummary, or astropy libraries*. For general instructions on how to manipulate the data file and/or read this data file with h5py, see the PESummary docs

In this notebook we use as an example the event GW190519_153544. The tar file containing the data that is used can be found here: https://dcc.ligo.org/LIGO-P2000223/public . We assume the tar file is unarchived in the same directory as this notebook.

The tar file contains several files, which includes two h5 files that contain PE samples. The difference between them is the distance prior used. Files with names of the form event_name.h5 contain samples from parameter estimation performed with $d_L^{2}$ prior, while those with event_name_comoving.h5 have samples that have been reweighted to a prior corresponding to a uniform merger rate per comoving volume in the rest frame of the source. Following the discussion in GWTC-2 paper, we use the results with the comoving prior in this notebook.

* We do not guarantee that the data release files can be read in with other packages.

First we import the key python modules

import matplotlib.pyplot as plt
import pesummary
from pesummary.io import read
print(pesummary.__version__)
import h5py
%matplotlib inline
%config InlineBackend.figure_format='retina'

0.11.0

Note that for pesummary<=0.9.1, seaborn<=0.10.1 is required.

The samples for each event is stored in the corresponding h5 file. This data file can be read either using h5py or using in using the pesummary read function. Each analysis file will contain several datasets. For a detailed description of what the names mean, see Table III and Table VIII of https://dcc.ligo.org/LIGO-P2000061/public.

file_name = './GW190519_153544/GW190519_153544_comoving.h5'

# Using h5py
with h5py.File(file_name, 'r') as f:
    print('H5 datasets:')
    print(list(f))

H5 datasets:
['C01:IMRPhenomD', 'C01:IMRPhenomPv2', 'C01:NRSur7dq4', 'C01:SEOBNRv4P', 'C01:SEOBNRv4PHM', 'C01:SEOBNRv4P_nonevol', 'PrecessingSpinIMR', 'PrecessingSpinIMRHM', 'PublicationSamples', 'ZeroSpinIMR', 'history', 'version']

# Using pesummary
data = read(file_name)
print('Found run labels:')
print(data.labels)

Found run labels:
['C01:IMRPhenomD', 'C01:IMRPhenomPv2', 'C01:NRSur7dq4', 'C01:SEOBNRv4P', 'C01:SEOBNRv4PHM', 'C01:SEOBNRv4P_nonevol', 'PrecessingSpinIMR', 'PrecessingSpinIMRHM', 'PublicationSamples', 'ZeroSpinIMR']

See the end of this notebook for more information about the different data sets.

For the remainder of the notebook, we demonstrate how to use pesummary to access and plot various aspects of the analysis.

The posterior samples can be extracted through the samples_dict property. These posterior samples are stored in a custom table structure. Below we load a particular dataset and show which parameters are available. For a detailed description of the meaning of most parameters, see definition of standard parameters

samples_dict = data.samples_dict
posterior_samples = samples_dict['PrecessingSpinIMRHM']

parameters = sorted(list(posterior_samples.keys()))
print(parameters)

['Npts', 'a_1', 'a_2', 'chi_eff', 'chi_p', 'chirp_mass', 'chirp_mass_source', 'comoving_distance', 'cos_iota', 'cos_theta_jn', 'cos_tilt_1', 'cos_tilt_2', 'dec', 'final_mass', 'final_mass_non_evolved', 'final_mass_source', 'final_mass_source_non_evolved', 'final_spin', 'final_spin_non_evolved', 'geocent_time', 'inverted_mass_ratio', 'iota', 'log_likelihood', 'luminosity_distance', 'mass_1', 'mass_1_source', 'mass_2', 'mass_2_source', 'mass_ratio', 'neff', 'p', 'peak_luminosity', 'peak_luminosity_non_evolved', 'phase', 'phi_1', 'phi_12', 'phi_2', 'phi_jl', 'ps', 'psi', 'psiJ', 'ra', 'radiated_energy', 'radiated_energy_non_evolved', 'redshift', 'spin_1x', 'spin_1y', 'spin_1z', 'spin_2x', 'spin_2y', 'spin_2z', 'symmetric_mass_ratio', 'theta_jn', 'tilt_1', 'tilt_2', 'total_mass', 'total_mass_source']

PrecessingSpinIMRHM analysis

pesummary allows for the user to easily make plots. As an example, we show the posterior distribution for chirp_mass_source plotted as a histogram and as a KDE.

fig = posterior_samples.plot('chirp_mass_source', type='hist')
_ = posterior_samples.plot('chirp_mass_source', type='hist',fig=fig, kde=True)
plt.show()

We may also easily generate a spin disk, showing the most probable direction of the spin vectors

fig = posterior_samples.plot(type='spin_disk', colorbar=True, annotate=False,
                            show_label=True, cmap='Blues')

Corner plots are very useful for spotting degeneracies between parameters. A corner plot can easily be generated using 'pesummary'

fig = posterior_samples.plot(type='corner',
                             parameters=['mass_1', 'mass_2', 'luminosity_distance', 'iota'])